Lehr- und Forschungseinheit für Datenbanksysteme

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Anna Beer

Research Assistant


Ludwig-Maximilians-Universität München
Lehrstuhl für Datenbanksysteme und Data Mining
Oettingenstraße 67
80538 München

Room: F 106
Phone: +49-89-2180-9309

Currently at Aarhus University

For master theses in the field of analyzing MD data, please write to beer@cs.au.dk 

Research Interests:

  • Methods for analyzing molecular dynamics data
  • Clustering, Subspace Clustering, Correlation Clustering, Spectral Clustering
  • High-Dimensional Data
  • Outlier Detection
  • Graphs and Networks





Supervised Theses:

  1. Multi-View Spectral Clustering on Single-View Data – Grouping High-Dimensional Representations of 3D Objects
  2. Accelerating Robust Spectral Clustering using the Nyström Method
  3. On Optimizing Parameter Settings for Clustering Algorithms
  4. Linear Unsupervised Correlation Clustering transferring Knowledge from Cluster Algorithms
  5. Statistic-based Internal Quality measures for Clustering
  6. A Multilabel Clustering Approach based on Attribute-Dependent Density
  7. Clustering using Inverse Heat Kernels
  8. Variants of Spectral Clustering
  9. kNN in High-Dimensional Data
  10. Interactive Spectral Clustering
  11. Comparison of Different Grid Construction Methods in Clustering
  12. Angle-Based Clustering
  13. A kNN-based Clustering Approach
  14. Anytime Outlier Detection based on Distributions and k-Means
  15. Subspace Clustering with kNN
  16. Gamification of Subspace Clustering via Visual Analytics
  17. Linear Correlation Clustering auf Basis der nächsten Nachbarn
  18. Chaindetection for DBSCAN
  19. Using a Semi-Pseudometric for Clustering High Dimensional Data
  20. Clustering Multidimensional Data using Fitted Grid Size
  21. Clustern von Punktmengen basierend auf dynamischer Dichte

Publications and Accepted Papers:

  1. Ullmann, T., Beer, A., Hünemörder, M., Seidl, T., & Boulesteix, A. L. (2022). Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. Advances in Data Analysis and Classification, 1-28.
  2. Beer, A., Stephan, L., & Seidl, T. (2021, December). LUCKe—Connecting Clustering and Correlation Clustering. In 2021 International Conference on Data Mining Workshops (ICDMW) (pp. 431-440). IEEE.
  3. Beer, A. (2021). On the edges of clustering (Doctoral dissertation, LMU Munich).
  4. Kazempour, D., Beer, A., Oelker, M., Kröger, P., & Seidl, T. (2021, September). Compound segmentation via clustering on Mol2Vec-based embeddings. In 2021 IEEE 17th International Conference on eScience (eScience) (pp. 60-69). IEEE.
  5. Sandra Obermeier, Anna Beer, Florian Wahl, Thomas Seidl (2021, September). Cluster Flow — an Advanced Concept for Ensemble-Enabling, Interactive Clustering. BTW 2021.
  6. Beer, A., Allerborn, E., Hartmann, V., & Seidl, T. (2021, March). KISS – A fast kNN-based Importance Score for Subspaces. EDBT 2021.
  7. Lohrer, A., Beer, A., Hünemörder, M., Lauterbach, J., Seidl, T., & Kröger, P. (2021). AnyCORE-An Anytime Algorithm for Cluster Outlier REmoval.
  8. D. Kazempour, A. Beer, P. Kröger, and T. Seidl. “I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering through piecewise-linear approximations of manifolds". In Proceedings of the 8th Workshop on High Dimensional Data Mining (HDM 2020) at the 20th IEEE International Conference on Data Mining (ICDM 2020). Virtual, Nov. 17–20, 2020.
  9. Beer, A., Seeholzer, D., Schüler, N. S., & Seidl, T. (2020, September). Angle-Based Clustering. SISAP 2020 (pp. 312-320).
  10. Beer, A., Kazempour, D., Busch, J., Tekles, A., Seidl, T. (2020, September). Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data. LWDA 2020.
  11. Beer, A., Hartmann, V., & Seidl, T. (2020, July). Orderings of Data-More Than a Tripping Hazard: Visionary. SSDBM 2020.
  12. Beer, A., Schüler, N. S., & Seidl, T. (2019). A Generator for Subspace Clusters. LWDA 2019.
  13. Kazempour, D., Beer, A., Schrüfer, O., & Seidl, T. (2019). Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents. LWDA 2019.
  14. Hünemörder, M. A. X., Kazempour, D., Beer, A., & Seidl, T. (2019). CODEC-Detecting Linear Correlations in Dense Clusters using coMAD-based PCA. LWDA 2019.
  15. Beer, A., Lauterbach, J. & Seidl, T. (2019, October). MORe++: k-Means based Outlier Removal on High-Dimensional Data. SISAP 2019.
  16. Held, J., Beer, A., & Seidl, T. Chain-detection Between Clusters. Datenbank-Spektrum, 1-12.
  17. Beer, A. & Seidl, T. (2019, July). Graph Ordering and Clustering - A Circular Approach. SSDBM 2019.
  18. Beer, A., Kazempour, D., Stephan, L., & Seidl, T. (2019, July). LUCK- Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function. SSDBM 2019.
  19. Beer, A., Kazempour, D., Baur, M., & Seidl, T. (2019, July). Human Learning in Data Science. In International Conference on Human-Computer Interaction (pp. 170-176). Springer, Cham.
  20. Kazempour, D., Beer, A., & Seidl, T. (2019, July). Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data. In International Conference on Human-Computer Interaction (pp. 184-189). Springer, Cham.
  21. Beer, A., Kazempour, D., & Seidl, T. (2019, March). Rock- Let the points roam to their clusters themselves. EDBT 2019 (pp. 630-633).
  22. Held, J., Beer, A., & Seidl, T. (2019, March). Chain-detection for DBSCAN. Datenbanksysteme für Business, Technologie und Web (BTW2019) (pp 173- 182).
  23. Kazempour, D., Beer, A., Herzog, F., Kaltenthaler, D., Lohrer, J. Y., & Seidl, T. (2018, October). FATBIRD: A Tool for Flight and Trajectories Analyses of Birds. In 2018 IEEE 14th International Conference on e-Science (e-Science) (pp. 75-82). IEEE.
  24. Kazempour, D., Beer, A., Lohrer, J. Y., Kaltenthaler, D., & Seidl, T. (2018, July). PARADISO: an interactive approach of parameter selection for the mean shift algorithm. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management (p. 26). ACM.